The problem of estimating and tracking the underlying, slowly time-varying frequency of a weak sinusoidal signal occurs in several areas of signal processing. There are a variety of known approaches to estimate the frequency of a harmonic signal. The straightforward approach of directly measuring the time difference between zero crossings and the number of cycles per second is very sensitive to noise in the signals. Approaches to overcome this problem have been proposed, such as techniques based on the Fourier transform, correlation, least square error techniques, recursive algorithms, chirp Z transform (CZT), adaptive notch filters, and Kalman filtering that estimates instantaneous frequency of the signal. In such problems the filter must deal with the inherent nonlinearity and also with extremely high noise levels. The Kalman filter is a recursive stochastic technique that gives an optimal estimation of state variables of a given linear dynamic system from noisy measurements. The Kalman filter gives a time-varying gain, which is not amenable to frequency domain analysis.